CN112164420B - Method for establishing genome scar model - Google Patents
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- G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract
The invention discloses a method for establishing a genome scar model and application thereof, comprising the following steps: (1) collecting known positive samples and negative samples to form a training set; (2) analyzing the CNV condition in the training set, and determining the type and the corresponding number of the CNV; (3) determining BRCAness positive events and BRCAness negative events; (4) and (3) training to obtain the weights of the CNVs of different types determined in the step (2) by a machine learning method according to BRCAness positive events and BRCAness negative events in a training set, and then accumulating the weights of the CNVs of different types to obtain a genome scar model for calculating the GSS. The invention can accurately predict the BRCAness state of the sample to be tested and can directly give an interpretation result according to the scar fraction of the genome.
Description
Technical Field
The invention belongs to the technical field of gene detection, and particularly relates to a method for establishing a genome scar model.
Background
Homologous Recombination Repair (HRR) is an important Repair mode for DNA double-stranded damage, and is often used in the precise Repair of harmful breaks in double-stranded DNA by cells. HRR is a complex signaling pathway involving multiple steps, in which a breast cancer susceptibility gene (BRCA1/2) is an important homologous recombination functionally related gene. HRR dysfunction, often referred to as Homologous Recombination Deficiency (HRD), results if mutations in the BRCA gene result in the loss of function of BRCA1 and BRCA2 proteins, and HRD is widely present as a tumor-driving event in breast, ovarian, prostate, and pancreatic cancers. Tumors that normally carry a mutation or abnormal expression of BRCA1/2 appear to be sensitive to platinum-based chemotherapeutic drugs and poly [ ADP-ribose ] polymerase inhibitors (PARPi). Therefore, the gene mutation detection of BRCA1/2 has prominent effect as the clinical classification and medication guidance of the diseases.
However, with the intensive research, BRCA gene mutation detection gradually fails to meet the existing clinical requirements, and drug effective populations enriched by BRCA gene mutation detection are low, and some treatment benefit populations are missed. For example, in triple negative breast cancer, 20% of patients carry mutations in the BRCA gene, whereas the overall response rate to platinum-based drugs is approximately 30% in the patient population. Meanwhile, in high-grade serous ovarian cancer, 30% of patients carry BRCA gene mutation, but the overall response rate of the patient population to the PARPi medicine is about 50%, which indicates that a part of patients with negative BRCA detection still respond to platinum or PARPi, so that part of treatment benefit populations are missed by BRCA detection. The reasons for omission mainly include: first, BRCA gene mutation detection is relatively limited. HRR function-related genes are numerous, and BRCA1/2 is two of them with higher mutation frequency. Analysis on the principle of drug action, as the result of the PROfound clinical study shows that the HRR related gene ATM defect has an effect on treating prostate cancer by PRAPi Olapari, the interest of HRR gene value (generally called BRCAness event) which can form synthetic lethal effect with platinum and PARPi is analyzed, and thus more and more clinical experiments are turning to other HRR related genes from the interest of BRCA. Secondly, mutation detection of the BRCA gene cannot cover all types of genomic abnormalities that cause loss of HRR function. In addition to gene mutations, methylation of the BCRA1 promoter region and Loss of Heterozygosity (LoH) within the BRCA gene region are also major causes of defects in HR function. Finally, the BRCA gene mutation detection result is complex to read, easy to omit and high in clinical application threshold. Currently, most practical guidelines for molecular genetic analysis of hereditary breast/ovarian cancer are introduced by several authorities such as the american society for medical genetics and genomics (ACMG) and the european union for molecular genetic diagnosis quality (EMQN). Classification of BRCA mutations: pathogenicity, possible pathogenicity, unknown significance, possible benign and benign, and slightly different levels of evidence cited in different guidelines pose significant barriers to clinical use.
For the above reasons, the need for novel clinical molecular markers that can be used for simple quantitative assessment of defects in cell homologous recombination repair is urgent. The search for molecular markers that are characteristic of downstream genomic mutations (including mutations, Copy Number Variations (CNVs), gene expression abnormalities) due to HRR deficiency is currently the main direction of research. In 2009 Olafur et al found that CNV mutation characteristics closely correlated with brcars, and in 2012, Abkevich et al found that the number of lohs in the whole genome significantly correlated with brcars events. In the same year, Popova et al found Large fragment transfer events (LST) in the gene to be associated with BRCA1/2 gene inactivation, while Birkbak et al also found telomere imbalance (TAI) to be associated with BRCAness events in triple negative breast cancers and significantly enriched in platinum-based treatment-sensitive populations. In 2016, the American Myriad company calculates HRD score (HRD score) quantitatively by counting the occurrence frequency of LoH, LST and TAI in the whole genome, and the statistical index can accurately predict BRCAness events and effectively enrich patient groups sensitive to treatment of platinum and PARPi. HRD scores can screen 40% more potentially beneficial patients than BRCA gene alone. In addition, in 2017, Davies et al found that a whole genome single base mutation pattern, a long and short fragment rearrangement pattern and an insertion deletion pattern are closely related to HRR defects, and the BRCAness event can be accurately predicted by Rogerster regression by combining the patterns and scoring HRD. However, the above methods have their limitations, e.g., the HRD score is simply a general addition of LoH, LST and TAI, and indeed TAI and LoH overlap in some cases, resulting in repeat counts, and furthermore some CNV types are not taken into account by the HRD score. While the model of Davies, though considered comprehensive, requires whole genome sequencing to count patterns of various mutation types, resulting in extremely expensive detection.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for establishing a genome scar model.
Another purpose of the invention is to provide application of the genome scar model established by the establishing method
The technical scheme of the invention is as follows:
a method for establishing a genome scar model comprises the following steps:
(1) collecting known positive samples and negative samples to form a training set;
(2) analyzing the CNV condition in the training set, and determining the type and the corresponding number of the CNV;
(3) determining BRCAness positive events and BRCAness negative events;
(4) training to obtain the weights of the CNVs of different types determined in the step (2) according to BRCAness positive events and BRCAness negative events in a training set by a machine learning method, and then accumulating the weights of the CNVs of different types to obtain a genome scar model for calculating GSS (genome scar fraction);
(5) collecting known positive samples and negative samples to form a test set, and obtaining the types and the corresponding number of CNV in the test set according to the step (2);
(6) and (4) substituting the result obtained in the step (5) into the genome scar model obtained in the step (4) to calculate the GSS of the test set, and verifying the genome scar model according to the score of the GSS.
In a preferred embodiment of the present invention, in step (2), sequencing analysis is performed on the training set obtained in step (1), CNV conditions in the sequencing analysis result are calculated, and regions with the same copy number variation that are adjacent to each other are connected into fragments to avoid repeated calculation, and the type and the corresponding number of CNVs are determined;
further preferably, the sequencing analysis is based on whole genome, whole exome, targeted capture sequencing or copy number variation chip.
In a preferred embodiment of the present invention, the brness positive events include: in any of BRCA1/2, one allele is mutated to be pathogenic or suspected to be pathogenic, and the other allele is heterozygous for the deletion; in any one of BRCA1/2, two pathogenic or suspected pathogenic mutations occur; one allele of BCRA1 was heterozygously deleted and the other allele promoter region was methylated.
In a preferred embodiment of the invention, the brness negative event is: the HRR-related gene is wild-type and the corresponding allele is not heterozygously deleted, or its promoter region is not methylated.
In a preferred embodiment of the present invention, the type of CNV in the step (2) is determined according to the length of the variant fragment, the type of the variant fragment, and the location of the variant fragment in the genome.
More preferably, the length of the variant fragment is divided into a short fragment of 5-10M, a medium fragment of more than 10M and 15M or less, and a long fragment of more than 15M.
Further preferably, the variant fragment types include loss of heterozygosity, unbalanced amplification of variant fragment strands, and balanced amplification of variant fragment strands.
Further preferably, the location of the variant fragment on the genome includes the location of the variant fragment at the telomere side, the location of the variant fragment in the centromeric region, and the location of the variant fragment other than at the telomere side and in the centromeric region.
One of the other technical schemes of the invention is as follows:
the genome scar model established by the establishing method is applied to the population related to HRR mutation enrichment.
The second technical scheme of the invention is as follows:
the genome scar model established by the establishing method is applied to the population with enriched platinum drug sensitivity.
The third technical scheme of the invention is as follows:
the genome scar model established by the establishing method is applied to the population enriching the PARPi drug sensitivity.
The invention has the beneficial effects that:
1. compared with BRCA gene mutation detection, the method can accurately predict the BRCAness state of the sample to be detected without detecting methylation of the BRCA1 promoter region and loss of heterozygosity of the BRCA gene. In addition, relative to the complex interpretation of BRCA mutations, the present invention can directly give interpretation results based on genomic scar scores.
2. Compared with BRCA gene mutation detection, the invention can enrich HRR related gene mutation patients.
3. Compared with BRCA gene mutation detection, the invention can enrich more platinum drug sensitive patients.
4. Compared with BRCA gene mutation detection, the invention can enrich more PARPi medication sensitive patients.
Drawings
FIG. 1 is a graph showing the results of the experiment in example 3 of the present invention.
FIG. 2 is a graph showing the results of the experiment in example 4 of the present invention.
FIG. 3 is a second graph showing the experimental results of example 4 of the present invention.
Detailed Description
The technical solution of the present invention will be further illustrated and described below with reference to the accompanying drawings by means of specific embodiments.
Example 1
FFPE samples and control blood samples of 110 and 18 patients with ovarian cancer as the types of test samples were collected as training and test sets for constructing genomic scar models, respectively. Then, a Homologous Recombination Defect (HRD) detection kit of Xiamen Ed biomedical science and technology Limited company is adopted for library construction and capture, and the kit comprises 35 HRR related genes and 7 ten thousand snp sites as capture regions. The captured and enriched DNA was finally sequenced on an Illumina Novaseq sequencer.
Raw off-machine data were aligned to human reference genomic sequences (version number hg19) by BWA (Li h.and Durbin r.2009). And generating a well-aligned BAM file as an input file of mutation and copy number variation. Among them, mutation detection was performed by the Varscan protocol (kobold, d.2012) and strand-specific copy number variation was performed by the sequenza protocol (Favero f.2015).
Brcable sample validation. Selecting the most common BRCAness event at present as a positive sample label, specifically comprising a.BRCA1/2 any one gene, one allele is subjected to pathogenic or suspected pathogenic mutation, and the other allele is subjected to heterozygosity loss; b, generating two pathogenic or suspected pathogenic mutations, namely functional deletion, in any gene of BRCA1/2; one allele of BCRA1 is subjected to heterozygosity loss, and the other allele promoter region is methylated, wherein the methylation of the BCRA1 promoter region is obtained by pyrosequencing technology.
The BRCAness negative sample confirmed that the HRR-associated mutant gene was wild-type and that LoH did not occur in the corresponding gene. The Xiamen Ed biological HRD detection reagent comprises HRR related genes as follows: ATM, FAM175A, FANCI, NBN, RAD51C, ATR, FANCA, FANCL, PALB2, RAD51D, ATRX, FANCC, FANCM, RAD50, RAD52, BAP1, FANCD2, KMT2D, RAD51, RAD54L, BARD1, FANCE, MDC1, RAD51B, SLX4, BLM, FANCF, MRE11A, WRN, XRCC2, BRCA1, FANCG, BRCA2, BRIP1, EMSY.
The copy number variant fragments of this example were classified according to their variant length, including short fragments (5-10M), medium fragments (greater than 10 and 15M or less), and long fragments (> 15M). Copy number variants can also be classified by variant type, including Loss of heterozygosity (LOH), strand imbalance Amplification (ASCNV), strand equilibrium amplification (BALANCE CNV, BCNV). Copy number variants can also be classified by the genomic location, including the variant being telomeric, within the centromeric region and the remaining other regions. The final copy number variants were classified into 27 types (i.e., length classification × variant type classification × genomic position classification ═ 27)
The treatment was performed as described above, with 68 brcats samples, 42 negative samples, 10 brcats samples and 8 negative samples in the training set. In order to prevent overfitting in the training process, only the types of the copy number variant fragments which occur in the training samples more frequently than the number of samples in the training set are reserved. And training copy number variant fragment type weight according to the sample BRCAness type by using logistic regression so as to construct a genome scar model. And calculating a sample with the GSS score of less than 0.5 of the sample to be detected by the genome scar model, and judging the sample as a BRCAness negative sample, and judging the sample with the GSS score of more than 0.5 as a BRCAness positive sample.
And in the test set, calculating the GSS of the sample to be tested by using the genome scar model, and judging the BRCAness state of the sample to be tested. And then, comparing the BRCAness states marked before the samples in the test set, wherein the genome scar models of 10 BRCAness positive samples can be accurately judged to be positive, and the genome scar models of 8 BRCAness negative samples can be accurately judged to be negative. Namely, the GSS of the genome scar model can accurately predict the BRCAness state of the sample, the sensitivity is 100%, the specificity is 100%, and the accuracy is 100%.
Example 2
191 test sample types, FFPE samples and control blood samples, were collected from patients with ovarian cancer. Then, a Homologous Recombination Defect (HRD) detection kit of Xiamen Add biomedical science and technology Limited company is adopted to construct and capture a library, sequencing is carried out on an Illumina Novaseq sequencer, and then the trained genome scar model in the embodiment 1 is adopted to calculate the GSS of the sample to be detected.
The relationship between GSS high grouping and HRR mutation population is counted as the following table
| No HRR-related Gene mutant group | Having HRR-related gene mutation group | |
| GSS low packet | 52 | 19 |
| GSS high packet | 56 | 64 |
GSS high-grade groups significantly enriched patients with HRR-associated gene mutations by hyper-geometric distribution test, P0.0003. Furthermore, GSS of the genomic scar model obtained in example 1 can enrich more genomically unstable patients than HRR-related genes.
Example 3
The FFPE samples and control blood samples were collected from 44 patients with ovarian cancer as the type of test sample, and the first treatment regimen after surgery was platinum chemotherapy. Then, a Homologous Recombination Defect (HRD) detection kit of Xiamen Add biomedical science and technology Limited company is adopted to construct and capture a library, sequencing is carried out on an Illumina Novaseq sequencer, and then the trained genome scar model in the embodiment 1 is adopted to calculate the GSS of the sample to be detected.
The effect of GSS enriched platinum drug-sensitive populations was assessed by comparing progression-free survival (PFS) of GSS high and low cohort patients. The results are shown in FIG. 1. As shown, the GSS status, i.e., GSS high score (GSS +) and GSS low score (GSS-) cohorts, showed significant differences in progression-free survival, and the patients in the GSS high cohort showed significantly longer progression-free survival with platinum therapy (P ═ 0.05), with PFS median in the GSS high cohort patients being 11 months and PFS median in the GSS low cohort patients being 8.5 months.
Example 4
FFPE samples and control blood samples were collected from 14 and 20 patients with ovarian cancer as type of test sample, who received a first line maintenance therapy and a post line treatment regimen of PARPi, respectively. Then, a Homologous Recombination Defect (HRD) detection kit of Xiamen Add biomedical science and technology Limited company is adopted to construct and capture a library, sequencing is carried out on an Illumina Novaseq sequencer, and then the trained genome scar model in the embodiment 1 is adopted to calculate the GSS of the sample to be detected.
In ovarian cancer patients receiving PARPi first-line maintenance therapy, as shown in fig. 2, the progression-free survival of patients was significantly different in the GSS high score (GSS +) and GSS low score (GSS-) cohorts, and significantly longer in the GSS high cohort (P ═ 0.03) with PARPi treatment, where the median PFS of the GSS high cohort was 10.5 months and the median PFS of the GSS low cohort was 7 months
In post-PARPi line-treated ovarian cancer patients, the Objective Remission Rates (ORR) were 38.5% (5/13) in the GSS high cohort and highest in the HRD high cohort (35.7%) and BRCA mutant group (33.3%), while ORR was 14.3% (1/7) in the GSS low cohort and lowest in the HRD low cohort (16.7%) and BRCA wild-type group (21.4%), with the results shown in fig. 3, i.e., the GSS of the genomic scar model obtained in example 1 enriched the PARPi-class drug-sensitive population.
The above description is only a preferred embodiment of the present invention, and therefore should not be taken as limiting the scope of the invention, which is defined by the appended claims.
Claims (8)
1. A method for establishing a genome scar model is characterized by comprising the following steps: the method comprises the following steps:
(1) collecting known positive samples and negative samples to form a training set;
(2) analyzing the CNV condition in the training set, and determining the type and the corresponding number of the CNV;
(3) determining BRCAness positive events and BRCAness negative events; brness positive events include: in any of BRCA1/2, one allele is mutated to be pathogenic or suspected to be pathogenic, and the other allele is heterozygous for the deletion; in any one of BRCA1/2, two pathogenic or suspected pathogenic mutations occur; one allele of BCRA1 is subjected to heterozygous deletion, and the promoter region of the other allele is methylated; brcable negative events were: the HRR-associated gene is wild-type and the corresponding allele is not heterozygously deleted, or its promoter region is not methylated;
(4) training to obtain the weights of the CNVs of different types determined in the step (2) according to BRCAness positive events and BRCAness negative events in a training set by a machine learning method, and then accumulating the weights of the CNVs of different types to obtain a genome scar model for calculating GSS;
(5) collecting known positive samples and negative samples to form a test set, and obtaining the types and the corresponding number of CNV in the test set according to the step (2);
(6) and (4) substituting the result obtained in the step (5) into the genome scar model obtained in the step (4) to calculate the GSS of the test set, and verifying the genome scar model according to the score of the GSS.
2. The method of establishing according to claim 1, wherein: the type of the CNV in the step (2) is determined according to the length of the variant fragment, the type of the variant fragment, and the location of the variant fragment in the genome.
3. The method of establishing according to claim 2, wherein: the length of the variant fragment is divided into a short fragment of 5-10M, a medium fragment of more than 10M and less than or equal to 15M and a long fragment of more than 15M.
4. The method of establishing according to claim 2, wherein: the variant fragment types include loss of heterozygosity, strand imbalance amplification of variant fragments, and strand equilibrium amplification of variant fragments.
5. The method of establishing according to claim 2, wherein: the location of the variant fragment on the genome includes the location of the variant fragment on the telomere side, the location of the variant fragment within the centromeric region, and the location of the variant fragment other than on the telomere side and within the centromeric region.
6. Use of the genomic scar model created by the method of creating any one of claims 1 to 5 for enriching the population associated with HRR mutation.
7. Use of the genomic scar model created by the method of creating any one of claims 1 to 5 for enriching the population with platinum drug sensitivity.
8. Use of the genomic scar model created by the method of creating any one of claims 1 to 5 for enriching a population with PARPi drug sensitivity.
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| JP2021547396A JP2022551205A (en) | 2020-09-07 | 2020-12-29 | How to construct a genomic scar model |
| EP20952323.2A EP4213152A1 (en) | 2020-09-07 | 2020-12-29 | Method for establishing genomic scar model |
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